A Text-Independent Speaker Authentication System for Mobile Devices

Thullier, Florentin; Bouchard, Bruno; Ménélas, Bob-Antoine J.

doi:10.3390/cryptography1020016

Cited by 9 publications

(8 citation statements)

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“…The majority of contemporary smart electronic gadgets have a microphone, allowing people to use voice in the authentication process [12]. Voice impersonation can recreate a voice, including pitch, tone to potentially fool this type of system.…”

Section: Voice Biometricsmentioning

confidence: 99%

Modern Authentication Methods: A Comprehensive Survey

Papathanasaki

Μαγλαράς

Ayres

2022

ACRT

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This paper presents a comprehensive investigation of modern authentication schemes. We start with the importance of authentication methods and the different authentication processes. Then we present the authentication criteria used and we perform a comparison of authentication methods in terms of universality, uniqueness, collectability, performance, acceptability, and spoofing. Finally, we present multi-factor authentication challenges and security issues and present future directions.

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Section: Voice Biometricsmentioning

confidence: 99%

Modern Authentication Methods: A Comprehensive Survey

Papathanasaki

Μαγλαράς

Ayres

2022

ACRT

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“…On-device-based Methods. Some works suggest running the systems on the device [35,97] and designing light encryption systems. Im et al in [43] propose a user-friendly, privacy-preserving face authentication system for smartphones to prevent malicious users from accessing the system.…”

Section: Secure Privacy-aware Biometric Systemsmentioning

confidence: 99%

Locally Authenticated Privacy-preserving Voice Input

Aloufi¹,

Nautsch²,

Haddadi³

et al. 2022

Preprint

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Increasing use of our biometrics (e.g., fingerprints, faces, or voices) to unlock access to and interact with online services raises concerns about the trade-offs between convenience, privacy, and security. Service providers must authenticate their users, although individuals may wish to maintain privacy and limit the disclosure of sensitive attributes beyond the authentication step, e.g., when interacting with Voice User Interfaces (VUIs). Preserving privacy while performing authentication is challenging, particularly where adversaries can use biometric data to train transformation tools (e.g., 'deepfaked' speech) and use the faked output to defeat existing authentication systems. In this paper, we take a step towards understanding security and privacy requirements to establish the threat and defense boundaries. We introduce a secure, flexible privacypreserving system to capture and store an on-device fingerprint of the users' raw signals (i.e., voice) for authentication instead of sending/sharing the raw biometric signals. We then analyze this fingerprint using different predictors, each evaluating its legitimacy from a different perspective (e.g., target identity claim, spoofing attempt, and liveness). We fuse multiple predictors' decisions to make a final decision on whether the user input is legitimate or not. Validating legitimate users yields an accuracy rate of 98.68% after cross-validation using our verification technique. The pipeline runs in tens of milliseconds when tested on a CPU and a single-core ARM processor, without specialized hardware.

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“…Thullier et al [12] proposed a text independent SV technique for cellular phones through amplifiers. This technique has the aim of improving the SV system via three fundamental tasks.…”

Section: Literature Surveymentioning

confidence: 99%

A Novel Optimized Deep Learner with Lightweighted Encryption-Based Speaker Verification

Rathinaraja¹,

Chandra²

2021

Preprint

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In the modern digitalized world, Speaker verification (SV) system is essential for authorizing the client’s credentials. To design an effective SV system, MGWOVSW-CAES-GMM system has been proposed. In this system, the Modified Grey Wolf Optimization (MGWO) technique was employed to optimize the variable sliding window size, FMPM features and training variables. The optimized features were watermarked and encrypted using a Chaotic-based Advanced Encryption Standard (CAES). Once the encryption process was completed, the encrypted features were forwarded to the recipient who executes the decryption and de-watermarking processes. At last, the decrypted features were classified using Gaussian Mixture Model (GMM) classifier. Conversely, MGWO has poor convergence rate and ineffective searching results. Hence, this article proposes an EEHOVSW-CAES-GMM system in which Enhanced Elephant Herding Optimization (EEHO) algorithm is applied instead of MGWO. On the contrary, the computational complexity of GMM classifier is high and its efficiency is less while increasing the number of features. For this reason, a Deep Neural Network (DNN) classifier is employed instead of GMM for recognizing the decrypted features and authorize the speaker’s identity. Besides, the parameters utilized in DNN topology are optimized using two different systems such as MGWOVSW-CAES-DNN and EEHOVSW-CAES-DNN for reducing the computational complexity and increasing the classification accuracy effectively when using more number of features. By using these classifiers, the speaker’s identity is verified and the attacks during the transmission are prevented with the highest security level.

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A Text-Independent Speaker Authentication System for Mobile Devices

Cited by 9 publications

References 0 publications

Modern Authentication Methods: A Comprehensive Survey

Modern Authentication Methods: A Comprehensive Survey

Locally Authenticated Privacy-preserving Voice Input

A Novel Optimized Deep Learner with Lightweighted Encryption-Based Speaker Verification

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